Development and evaluation of artificial intelligence based maximum power point tracking for photovoltaic systems across diverse weather conditions

Abstract

An essential control mechanism for solar panels, maximum power point tracking (MPPT) constantly adjusts the operating point to maximize power extraction from changing environmental conditions, ensuring that the panels run at peak efficiency. To maximize energy yield, improve overall system performance, and add to the financial feasibility of solar installations, MPPT is crucial in today's energy landscape, which is increasingly focused on clean and renewable sources. In this study, we test four popular photovoltaic maximum power point tracking (MPPT) algorithms in different weather scenarios: perturb and observe (P&O), fuzzy logic, grey wolf optimizer (GWO), and horse herd optimization (HHO). Key parameters such as efficiency, responsiveness to partial shading, and adaptability to changing environmental conditions are analyzed using MATLAB models to evaluate each algorithm's performance in depth. The results show where each algorithm excels and where it falls short, and the research stands out by incorporating new features into the models. Our study seeks to provide valuable insights for the development of photovoltaic (PV) MPPT algorithms, guiding future research and applications in the ever-changing field of renewable energy systems. We will focus on making these algorithms more flexible in dynamic environments and resilient in partial shading situations.